Electronic messaging has evolved into a new standard communication medium. More and more customers are now using electronic messages to communicate, promote, follow-up and as a replacement when face to face communication is not possible. Therefore, users may receive several hundred emails, piled up in the inbox, even over a short period of time. The users may also have hundreds of unopened emails if an active email address has fallen into the hands of spammers or online advertisers.
It is of utmost interest to have a mechanism that simplifies the task of responding to large volumes of electronic messages by optimizing the time spent on message processing by scanning their inbox, checking sender details and subjects in order to prioritize some messages for attention over others. Thus, the main goal of email management is to identify messages with a high value of user-perceived importance, since it is generally understood that the action that a user takes on a message, e.g., read, reply, file or delete, largely depends on the user-perceived importance of the message.
There already exists a wide range of techniques for redesigning email interfaces to help users quickly identify important emails in their inbox. For example, existing approaches mostly prioritize emails based on a classifier that is trained using supervised learning algorithms. However, the features used by these approaches for classifier learning may not work well for very brief messages with too few words (sparse data) or long messages with too many words (noisy data).
The issues mainly faced in the electronic message management are prioritizing the electronic messages without making changes to the internal architecture or design of the associated electronic device.